# Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector

arXiv: https://arxiv.org/abs/1811.05616 · HTML: https://mlsanity.com/paper/1811.05616/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs](https://mlsanity.com/paper/2105.11225/) (2021)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2202.13352/) (2022)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)

## Abstract (arXiv)

Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.